SoroushVahidi/scidocs huggingface dataset:
Mutual Information Based Semi-Global Stereo Matching on the GPU
(2008)

Real-time stereo matching is necessary for many practical applications, including robotics. There are already many real-time stereo systems, but they typically use local approaches that cause object boundaries to be blurred and small objects to be removed. We have selected the Semi-Global Matching (SGM) method for implementation on graphics hardware, because it can compete with the currently best global stereo methods. At the same time, it is much more efficient than most other methods that produce a similar quality. In contrast to previous work, we have fully implemented SGM including matching with mutual information, which is partly responsible for the high quality of disparity images. Our implementation reaches 4.2 fps on a GeForce 8800 ULTRA with images of 640×480 pixel size and 128 pixel disparity range and 13 fps on images of 320×240 pixel size and 64 pixel disparity range.
Visual Correspondence Using Energy Minimization and Mutual Information
(2003)

We address visual correspondence problems without assuming that scene points have similar intensities in different views.This situation is common, usually due to non-lambertian scenes or to differences between cameras. We use maximization of mutual information, a powerful technique for registering images that requires no a priori model of the relationship between scene intensities in different views. However, it has proven difficult to use mutual information to compute dense visual correspondence. Comparing fixed-size windows via mutual information suffers from the well-known problems of fixed windows, namely poor performance at discontinuities and in low-texture regions. In this paper, we show how to compute visual correspondence using mutual information without suffering from these problems. Using a simple approximation, mutual information can be incorporated into the standard energy minimization framework used in early vision. The energy can then be efficiently minimized using graph cuts, which preserve discontinuities and handle low-texture regions. The resulting algorithm combines the accurate disparity maps that come from graph cuts with the tolerance for intensity changes that comes from mutual information.
Robust feature selection and robust PCA for internet traffic anomaly detection
(2012)

Robust statistics is a branch of statistics which includes statistical methods capable of dealing adequately with the presence of outliers. In this paper, we propose an anomaly detection method that combines a feature selection algorithm and an outlier detection method, which makes extensive use of robust statistics. Feature selection is based on a mutual information metric for which we have developed a robust estimator; it also includes a novel and automatic procedure for determining the number of relevant features. Outlier detection is based on robust Principal Component Analysis (PCA) which, opposite to classical PCA, is not sensitive to outliers and precludes the necessity of training using a reliably labeled dataset, a strong advantage from the operational point of view. To evaluate our method we designed a network scenario capable of producing a perfect ground-truth under real (but controlled) traffic conditions. Results show the significant improvements of our method over the corresponding classical ones. Moreover, despite being a largely overlooked issue in the context of anomaly detection, feature selection is found to be an important preprocessing step, allowing adaption to different network conditions and inducing significant performance gains.
On the Analysis and Design of Low-Loss Single-Pole Double-Throw W-Band Switches Utilizing Saturated SiGe HBTs
(2014)

This paper describes the analysis and design of saturated silicon-germanium (SiGe) heterojunction bipolar transistor (HBT) switches for millimeter-wave applications. A switch optimization procedure is developed based on detailed theoretical analysis and is then used to design multiple switch variants. The switches utilize IBM's 90-nm 9HP technology, which features SiGe HBTs with peak f T/ fmax of 300/350 GHz. Using a reverse-saturated configuration, a single-pole double-throw switch with a measured insertion loss of 1.05 dB and isolation of 22 dB is achieved at 94 GHz after de-embedding pad losses. The switch draws 5.2 mA from a 1.1-V supply, limiting power consumption to less than 6 mW. The switching speed is analyzed and the simulated turn-on and turn-off times are found to be less than 200 ps. A technique is also introduced to significantly increase the power-handling capabilities of saturated SiGe switches up to an input-referred 1-dB compression point of 22 dBm. Finally, the impact of RF stress on this novel configuration is investigated and initial measurements over a 48-h period show little performance degradation. These results demonstrate that SiGe-based switches may provide significant benefits to millimeter-wave systems.
New Canonical Representations by Augmenting OBDDs with Conjunctive Decomposition (Extended Abstract)
(2017)

We identify two families of canonical representations called ROBDD[∧î]C and ROBDD[∧T̂ ,i]T by augmenting ROBDD with two types of conjunctive decompositions. These representations cover the three existing languages ROBDD, ROBDD with as many implied literals as possible (ROBDDL∞), and AND/OR BDD. We introduce a new time efficiency criterion called rapidity which reflects the idea that exponential operations may be preferable if the language can be exponentially more succinct. Then we demonstrate that the expressivity, succinctness and operation rapidity do not decrease from ROBDD[∧T̂ ,i]T to ROBDD[∧î]C , and then to ROBDD[∧ î+1 ]C . We also demonstrate that ROBDD[∧î]C (i > 1) and ROBDD[∧T̂ ,i]T are not less tractable than ROBDD-L∞ and ROBDD, respectively. Finally, we develop a compiler for ROBDD[∧∞̂]C which significantly advances the compiling efficiency of canonical representations.
A new dynamic foot abduction orthosis for clubfoot treatment.
(2007)

Recurrent clubfoot deformity after successful initial correction with the use of the Ponseti method continues to be a common problem and is often caused by noncompliance with wear of the traditional foot abduction brace. The purpose of this study was to assess the results of a newly designed dynamic foot abduction orthosis in terms of (1) parental compliance and (2) effectiveness in preventing recurrent clubfoot deformities. Twenty-eight patients (49 clubfeet) who were treated with a dynamic foot abduction orthosis in accordance with the Ponseti method were included in this study. Of the 28 patients, 18 had idiopathic clubfeet (31 clubfeet), 2 had complex idiopathic clubfeet (4 clubfeet), 5 had myelodysplasia (8 clubfeet), and 3 were syndromic (6 clubfeet). The mean duration of follow-up was 29 months (range, 24-36 months). Noncompliance was reported in only 2 (7.1%) of the 28 patients in the new orthosis compared with the authors' previously reported 41% (21/51) noncompliance rate in patients treated with the use of the traditional foot abduction brace. The two patients in this study, in which parents were noncompliant with orthosis wear, developed recurrent deformities. There were 2 patients (7%) who experienced skin blistering in the new orthosis compared with 12 (23.5%) of 51 patients who experienced blistering with the use of traditional abduction brace in the authors' previously reported study. Logistic regression modeling compliance and recurrence revealed that noncompliance with the foot abduction orthosis was most predictive of recurrence of deformity (odds ratio, 27; 95% confidence interval, 2.2-326; P = 0.01). The articulating foot abduction orthosis is well tolerated by patients and parents and results in a higher compliance rate and a lower complication rate than what were observed with the traditional foot abduction orthosis.
A review on License Plate Recognition system algorithms
(2016)

License plate recognition(LPR)system contributed to facilitate the human work and make it easier. It is an image processing technology, which uses number plate to identify the vehicles. This paper aims to investigate in the existing LPR research by categorizing existing methods by analyzing the pros/cons of these features, and comparing them in terms of the algorithms that involve and recognition performance. We focus on the recent studies and their limitations, since each country has a different environment and format of licence plate.
A Review of the Evolution of Vision-Based Motion Analysis and the Integration of Advanced Computer Vision Methods Towards Developing a Markerless System
(2018)

BACKGROUND
The study of human movement within sports biomechanics and rehabilitation settings has made considerable progress over recent decades. However, developing a motion analysis system that collects accurate kinematic data in a timely, unobtrusive and externally valid manner remains an open challenge.


MAIN BODY
This narrative review considers the evolution of methods for extracting kinematic information from images, observing how technology has progressed from laborious manual approaches to optoelectronic marker-based systems. The motion analysis systems which are currently most widely used in sports biomechanics and rehabilitation do not allow kinematic data to be collected automatically without the attachment of markers, controlled conditions and/or extensive processing times. These limitations can obstruct the routine use of motion capture in normal training or rehabilitation environments, and there is a clear desire for the development of automatic markerless systems. Such technology is emerging, often driven by the needs of the entertainment industry, and utilising many of the latest trends in computer vision and machine learning. However, the accuracy and practicality of these systems has yet to be fully scrutinised, meaning such markerless systems are not currently in widespread use within biomechanics.


CONCLUSIONS
This review aims to introduce the key state-of-the-art in markerless motion capture research from computer vision that is likely to have a future impact in biomechanics, while considering the challenges with accuracy and robustness that are yet to be addressed.
On the Efficiency of Far-Field Wireless Power Transfer
(2015)

Far-field wireless power transfer (WPT) is a promising technique to resolve the painstaking power-charging problem inherent in various wireless terminals. This paper investigates the power transfer efficiency of the WPT segment in future communication systems in support of simultaneous power and data transfer, by means of analytically computing the time-average output direct current (DC) power at user equipments (UEs). In order to investigate the effect of channel variety among UEs on the average output DC power, different policies for the scheduling of the power transfer among the users are implemented and compared in two scenarios: homogeneous, whereby users are symmetric and experience similar path loss, and heterogeneous, whereby users are asymmetric and exhibit different path losses. Specifically, if opportunistic scheduling is performed among N symmetric/asymmetric UEs, the power scaling laws are attained by using extreme value theory and reveal that the gain in power transfer efficiency is lnN if UEs are symmetric whereas the gain is N if UEs are asymmetric, compared with that of conventional round-robin scheduling. Thus, the channel variety among UEs inherent to the wireless environment can be exploited by opportunistic scheduling to significantly improve the power transfer efficiency when designing future wireless communication systems in support of simultaneous power and data transfer.
Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization
(2018)

We propose a simple and easy to implement neural network compression algorithm that achieves results competitive with more complicated state-of-the-art methods. The key idea is to modify the original optimization problem by adding K independent Gaussian priors (corresponding to the k-means objective) over the network parameters to achieve parameter quantization, as well as an `1 penalty to achieve pruning. Unlike many existing quantization-based methods, our method uses hard clustering assignments of network parameters, which adds minimal change or overhead to standard network training. We also demonstrate experimentally that tying neural network parameters provides less gain in generalization performance than changing network architecture and connectivity patterns entirely.
Trust and risk in e-government adoption
(2005)

Citizen confidence in the competence of the government and the reliability of the technology used to implement egovernment initiatives is imperative to the wide-spread adoption of e-government. This study analyzes how citizens’ trust in technology and government affect their willingness to engage in e-government transactions. We proposes a model of egovernment trust composed of disposition to trust, institution-based trust (IBT), characteristic-based trust (CBT) and perceived risk. Data were collected via a survey of 214 citizens ranging in age from 14 to 83 old. The model was tested using Structural Equation Modeling techniques. Results indicate that disposition to trust positively affects IBT and CBT trusts, which in turn affect intentions to use an e-government service. CBT trust also affects negatively perceived risk, which affects use intentions as well. Implications for practice and research are discussed.
Recovery for overloaded mobile edge computing
(2017)

Mobile edge computing (MEC) allows the use of its services with low latency, location awareness and mobility support to make up for the disadvantages of cloud computing. But an overloaded MEC system or any MEC system failure significantly degrades quality of experience (QoE) and negates the advantages of MEC. In this paper, we propose two different recovery schemes for overloaded or broken MEC. One recovery scheme is where an overloaded MEC offloads its work to available neighboring MECs within transfer range. The other recovery scheme is for situations when there is no available neighboring MEC within transfer range. The second scheme accesses user devices of a neighboring MEC adjacent to the overloaded MEC as ad-hoc relay nodes in order to bridge the transfer disconnect between two MECs. Numerical experiments demonstrated the performance and verified the possibility of the proposed schemes, providing insightful results for potential future
Encoding Sentiment Information into Word Vectors for Sentiment Analysis
(2018)

Santa Fe, New Mexico, USA, August 20-26, 2018. 997 Encoding Sentiment Information into Word Vectors for Sentiment Analysis Zhe Ye♠ Fang Li♠ Timothy Baldwin♥ ♠ Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China ♥ School of Computing and Information Systems, The University of Melbourne, Victoria, 3010, Australia {yezhejack, fli}@sjtu.edu.cn, tb@ldwin.net
Seventh sense technology
(2015)

Seventh Sense Technology is a technology used for the human electronic interaction using natural hand gestures. The hand gestures can be used to control any micro controller based devices or robots at distant places using Zigbee or GSM Module or Satellite transceiver. The "Seventh Sense Technology" is easy to implement with reduced implementation cost and it opens a wide area of research for the next generation and a greater development in almost all areas of current interest. Seventh Sense Technology uses the Sixth Sense Technology and its advancements to control the autonomous robots or any microcontroller based devices on human will. Currently autonomous robots or any microcontroller based devices are designed for a single purpose, but with this technology robots and those devices can be made to achieve multidimensional purposes. Even the costlier sensors can be replaced with the low cost night vision cameras. It opens a way to control robots or any microcontroller based devices at any distance with natural hand gestures of human beings. It also has the provision of customizable virtual keyboard for giving more actions to distant devices. It is going to be a greater field of research that also paves a lot of attention in this current world of technology.
Semantic labeling of 3D point clouds with object affordance for robot manipulation
(2014)

When a robot is deployed it needs to understand the nature of its surroundings. In this paper, we address the problem of semantic labeling 3D point clouds by object affordance (e.g., `pushable', `liftable'). We propose a technique to extract geometric features from point cloud segments and build a classifier to predict associated object affordances. With the classifier, we have developed an algorithm to enhance object segmentation and reduce manipulation uncertainty by iterative clustering, along with minimizing labeling entropy. Our incremental multiple view merging technique shows improved object segmentation. The novel feature of our approach is the semantic labeling that can be directly applied to manipulation planning. In our experiments with 6 affordance labels, an average of 81.8% accuracy of affordance prediction is achieved. We demonstrate refined object segmentation by applying the classifier to data from the PR2 robot using a Microsoft Kinect in an indoor office environment.
Patterns, Causes, and Consequences of Anthropocene Defaunation
(2016)

Anthropocene defaunation, the global extinction of faunal species and populations and the decline in abundance of individuals within populations, has been predominantly documented in terrestrial ecosystems, but indicators suggest defaunation has been more severe in freshwater ecosystems. Marine defaunation is in a more incipient stage, yet pronounced effects are already apparent and its rapid acceleration seems likely. Defaunation now impacts the planet’s wildlife with profound cascading consequences, ranging from local to global coextinctions of interacting species to the loss of ecological services critical for humanity. Slowing defaunation will require aggressively reducing animal overexploitation and habitat destruction; mitigating climate disruption; and stabilizing the impacts of human population growth and uneven resource consumption. Given its omnipresence, defaunation should receive status of major global environmental change and should be addressed with the same urgency as deforestation, pollution, and climatic change. Global action is needed to prevent defaunation’s current trajectory from catalyzing the planet’s sixth major extinction. 333 Review in Advance first posted online on August 26, 2016. (Changes may still occur before final publication online and in print.) Changes may still occur before final publication online and in print A nn u. R ev . E co l. E vo l. Sy st . 2 01 6. 47 . D ow nl oa de d fr om w w w .a nn ua lr ev ie w s. or g A cc es s pr ov id ed b y U ni ve rs ity o f C al if or ni a Sa nt a B ar ba ra o n 10 /2 0/ 16 . F or p er so na l u se o nl y. ES47CH15-Dirzo ARI 19 August 2016 14:55
Haptic Virtual Fixtures for Robot-Assisted Manipulation
(2005)

Haptic virtual fixtures are software-generated force and position signals applied to human operators in order to improve the safety, accuracy, and speed of robot-assisted manipulation tasks. Virtual fixtures are effective and intuitive because they capitalize on both the accuracy of robotic systems and the intelligence of human operators. In this paper, we discuss the design, analysis, and implementation of two categories of virtual fixtures: guidance virtual fixtures, which assist the user in moving the manipulator along desired paths or surfaces in the workspace, and forbidden-region virtual fixtures, which prevent the manipulator from entering into forbidden regions of the workspace. Virtual fixtures are analyzed in the context of both cooperative manipulation and telemanipulation systems, considering issues related to stability, passivity, human modeling, and applications.
Hard Drive Failure Prediction Using Big Data
(2015)

We design a general framework named Hdoctor for hard drive failure prediction. Hdoctor leverages the power of big data to achieve a significant improvement comparing to all previous researches that used sophisticated machine learning algorithms. Hdoctor exhibits a series of engineering innovations: (1) constructing time dependent features to characterize the Self-Monitoring, Analysis and Reporting Technology (SMART) value transitions during disk failures, (2) combining features to enable the model to learn the correlation among different SMART attributes, (3) regarding circumstance data such as cluster workload, temperature, humidity, location as related features. Meanwhile, Hdoctor collects/labels samples and updates model automatically, and works well for all kinds of disk failure prediction in our intelligent data center. In this work, we use Hdoctor to collect 74,477,717 training records from our clusters involving 220,022 disks. By training a simple and scalable model, our system achieves a detection rate of 97.82%, with a false alarm rate (FAR) of 0.3%, which hugely outperforms all previous algorithms. In addition, Hdoctor is an excellent indicator for how to predict different hardware failures efficiently under various circumstances.
Introduction to Nonlinear Optimization - Theory, Algorithms, and Applications with MATLAB
(2014)

introduction to nonlinear optimization theory algorithms introduction to nonlinear optimization theory algorithms introduction to nonlinear optimization theory algorithms introduction to nonlinear optimization theory algorithms chapter 16: introduction to nonlinear programming nonlinear programming: concepts, algorithms and applications theory, algorithms, and applications with matlab introduction to nonlinear optimization epubsam course: math 657|optimization theory time: spring 2016, 5 convex optimization: modeling and algorithms nonlinear programming 13 mit massachusetts institute an introduction to nonlinear optimization theory ebook nonlinear optimization lecture notes for the course mat a and trends in optimization with e applications solving optimization problems using the matlab siam bestsellers 2000–2015 order author title code 2015 optimization an introduction imperial college london [review published in siam review, vol. 58, issue 1, pp. 164.] nonlinear optimization and applications springer convex analysis and nonlinear optimization theory and examples appm 4720/5720 advanced topics in convex optimization lecture slides on nonlinear programming based on lectures convex optimization — boyd & vandenberghe
A Survey of Game Theory as Applied to Network Security
(2010)

Network security is a complex and challenging problem. The area of network defense mechanism design is receiving immense attention from the research community for more than two decades. However, the network security problem is far from completely solved. Researchers have been exploring the applicability of game theoretic approaches to address the network security issues and some of these approaches look promising. This paper surveys the existing game theoretic solutions which are designed to enhance network security and presents a taxonomy for classifying the proposed solutions. This taxonomy should provide the reader with a better understanding of game theoretic solutions to a variety of cyber security problems.